docs: def _set_oversampling(self, value):
          """
          This is a private method because it's used in the initializer by the
          ``oversampling``
          """
  
          try:
              value = np.atleast_1d(value).astype(float)
              if len(value) == 1:
                  value = np.repeat(value, 2)
          except ValueError:
              raise ValueError('Oversampling factors must be float')
          if np.any(value <= 0):
              raise ValueError('Oversampling factors must be greater than 0')
  
          self._oversampling = value